TrendForce analyst Jimmy Liu points out that the traditional architectures of cloud computing systems have led the market for many years, and have created new business opportunities such as cloud storage and big data analysis. However, with increasingly huge amounts of data and the rise of real-time computing, traditional architectures have been unable to meet future demand. With a decentralized structure, edge computing integrates network, computing, storage, and self-management at the edge of field devices and gateways, etc., helping to realize real-time response of field devices and enhance the efficiency of data collection and advanced application. Moreover, edge computing can reduce the cost compared with traditional architectures.
Supply chains have been developing industry standards and the edge computing ecosystem
Many alliances have been setting new standards for edge computing, for this new paradigm will bring changes to the architectures and actual applications in the market. For instance, the Multi-Access Edge Computing (MEC) of The European Telecommunications Standards Institute (ETSI), the OpenFog Reference Architecture for Fog Computing, and the Edge Computing Consortium led by Huawei have been actively developing reference architectures to establish the business ecosystems .
In addition, many companies have rolled out their edge computing solutions, such as Azure IoT Edge launched by Microsoft, which puts machine learning, advanced analytics and AI services at front-end IoT devices which are closer to the source of data; chip IP provider Arm also introduced Mbed Edge, a computing platform to assist in protocol translation, gateway management and edge computing; in addition, the rest of the industry chain, including server providers, network equipment providers, industrial computer providers, traditional manufacturers, open source organizations, have all introduced corresponding solutions.
Implementation of AI and 5G will depend on assistance of edge computing
Edge computing, which has become a focus of discussion since 2017, will have significant implications on AI and 5G. Liu notes that, AI used to rely on powerful cloud computing capabilities for data analysis and algorithm, but with the advancement of chips and the development of edge computing platform, field devices and gateways have been entitled basic AI abilities, which allow them to assist in the initial data screening and analysis, immediate response to requirements, etc. This advantage can further improve existing services in industries, smart cities and consumer markets, such as real-time alerts, security system, smart assistant and predictive maintenance, etc.
Edge computing is also an important technological transformation for 5G. Compared with 3G and 4G era, 5G network features diverse applications and network demands. Therefore, 5G network must offer corresponding solutions for different applications and requirements. In this condition, edge computing can provide mobile users with better network quality and lower latency, and allow telecom operators to launch more innovative services.